Rob Wilson on Bristlecones

Rob Wilson sent in a post on another thread arguing that bristlecones are not as bad a proxy as I would have everyone believe. Unlike realclimate, opposing views are not censored here. In fact, I’m happy to highlight them. I’ll read Rob’s note and reply on an another occasion. I’ll only note now that, in our discussions of bristlecones, especially in EE [2005], we relied on specialist publications such as Graybill and Idso [1993], Hughes and Funkhouser [2003], and even (implicitly) IPCC 2AR, as questioning the validity of bristlecones as a temperature proxy, rather than arguing the point ourselves from first principles; otherwise I won’t editorialize further here, but will re-visit the topic on another occasion.

Rob: Certainly, if these data dominate a NH reconstruction through the use of PCA, this is probably not an ideal situation where presumably a NH reconstruction should be representative of the NH.

However, how valid are the BP data as a “Ålocal’ temperature proxy?

At the link below, I have uploaded some pictures comparing BP data with the North American (NA) mean series from our 2006 paper. We never included the BP data in our paper as we wanted to use as high latitude TR data as possible. However, the inclusion of the BP would have made little difference to the final NH reconstruction, although it would have depressed the MWP a little relative to the recent period.

Figure 1 – a comparison of our NA mean with a RCS chronology developed using BP RW data from three sites: Hermit Hill (N = 38; 1048-1983) and Windy Ridge (N = 29; 1050-1985) from Colorado and Sheep Mountain (N = 71; 0 — 1990) from California. The time-series have been normalized to the 1200-1750 period.

A couple of interesting points:

1. at least for the 1100-20th century period, there is a surprisingly strong common signal. From this comparison alone, one could conclude that, although the BP data represent a relatively small region (in a global sense), the data do seem to pick up the multi-decadal to centennial scale variability of the larger NA mean series. The deviation between the series prior to 1100 may simply represent the decreasing replication of NA sites – there are only two sites that go back prior to 1100 in the NA mean. As stated in our paper, I do not feel there is enough data prior to ~1400 anyway.

The BP RCS chronology correlates with “Ålocal’ July-September mean CRU gridded temperatures at 0.38 (Durbin-Watson = 1.70). This is admittedly not particularly strong (NB. no autocorrelation in the residuals though), but with the reasonable comparison with the NA mean, it does seem to suggest that temperature is likely the dominant controlling factor – especially at decadal to longer time-scales.

Now, I do not deny that all sorts of other factors may also influence growth and I am sure someone will say – “Åwell how about all the none explained variance?.’ Well – Dendro-reconstructions generally explain anywhere from 30-60% of the variance of the climate parameter that one is trying to reconstruct. This is a modeled mean response over a particular [calibration] time-period and does not look at the more complex situation for each year. Using regression, it is easy to test if multiple climate parameters (i.e. temperature and precipitation) effect the growth of trees. It is generally the case, that if the tree site is carefully selected (i.e. high elevation/latitude treeline for a temperature signal), then precipitation will have a minimal effect on growth and over the period of calibration, the correlation with precipitation will be close to zero.

2. I purposely normalized the data to the 1200-1750 period so that any possible inflation of BP index values due to CO2 fertilization would be accentuated relative to the NA mean. Interestingly, the NA mean index values are higher than the BP data. Assuming that no CO2 fertilization effect biases the NA mean (there is no evidence for this at all), then I see no evidence of this effect in the BP data either.

Figure 2 – comparison of the NA RCS mean with the recently published annual temperature reconstruction for the Colorado region from Salzer and Kipfmueller (2005). They also used BP data – independent to the data I used. There model explains 46% of the variance (DW = 1.64), so is appears to be a much stronger temperature proxy than the one I showed in Figure 1. Again we see reasonable coherence between 1100-1900 and again the BP data do not show higher values in the 20th century than the NA RCS mean. Salzer and Kipfmueller did not use RCS for detrending so it is possible that they have lost some long term information.

“¢’¬?”¢’¬?”¢’¬?”¢’¬?”¢’¬?”¢’¬?”¢’¬?-

Take home message – I do not think the BP data are as bad as Steve would have us believe.

Thanks to Steve for being the gracious host, and for Rob for having the grace and patience to lay out some of his arguments. Would that this were the way all such disagreements were handled, frankly and in broad daylight.

This is much better than the communal feces fling that hit-and-run or ‘true believer’ commenters (of any stripe) set off. It could also avoid elision of details I see in some peer reviewed (and peer rejected) work, so please, keep it going, gentlemen.

I’d like to thank Rob, too, for a cordial and scientific presentation of data and conclusions, and Steve for encouraging a fair discussion.

A few questions occur to me. First is, why are the high-frequency displacements (noise?) as intense in the BP series as in the NA mean (Figure 1)? I’d have thought that the mean series, containing far more data, ought to show much lower noise. Perhaps the high-frequency component isn’t really noise?

A second more general question that, for me, goes to the heart of the dendroclimatology reconstructions, is how does one know that a tree series that correlates with recent temperature remains correlated with past temperatures? The divergence problem implies variability in tree ring response, and I’d like to know how one is assured that a linear responder today was also a linear responder yesterday.

The final question refers to the last century trends in both figures. Both BP and NA series show an increasing slope, certainly from 1900 through to about 2000 (although the NA mean in Figure shows an interesting steep decline over about the last decade). However, the instrumental record* for the continental US shows the 20th century temperature trend to be net almost flat. More detail shows a small positive T-slope from 1910-1940, a small decline to 1970, and a small positive trend to 1997, all contained within a 1 degree range. This doesn’t seem to be what either the BP series or the NA mean show. So how does one establish that there is a correlation of tree rings to temperature?

My understanding was that the person/people who made the “Bristlecone” measurements compared the proxies to the local temperature record and found that the 20th century “spike” was not associated with a temperature increase.

If that understanding is correct, then it means one of two things, I think. The fact that the “North American Mean Temperature” index correlates with the proxies which are KNOWN not to be a good proxy of temperatures during the 20th century, means that either the correlation in the 20th century co-incidental, or else the “North American Mean Temperature” is biased by the same factor(s) in the 20th century which make the “bristlecones” deviate from being a temperature proxy.

Now maybe Rob is correct and the “bristlecones” are a good temperature proxy for the rest of the time, and the correlation is a sign of that. However, given that we know that’s unlikely to be the case during the 20th century for the above reason, what gives us confidence that it isn’t the same confounding factor(s) responsible for the correlation the rest of the time?

I know this sounds like a broken record but I’m afraid it’s very simple. Defending a proxy as being a good proxy temperature, when during the period of overlap between the proxy and an instrumental record there is clearly not good correlation, is a difficult exercise. I don’t think comparing it to another proxy reconstruction is a fundamentally good way of proving the goodness of the one proxy, since there’s no guarantee that the other reconstruction doesn’t suffer from the same divergences. In short, how do you tell the difference between good correlation between good temperature proxies, and correlation between similarly flawed temperature proxies, which may in fact be proxying something else altogether, albeit the same thing in both cases?

Indeed, someone needs to do an audit of the GISS stuff. There’s clearly something wrong with either GISS or the Climate Research study cited in the previous post. But I suppose the data are not archived, as seems to be the typical practice in climatology.

My issues with Bristlecones:
* The climate they live in features drastic temperature extremes – daily, day to day, annually, and year to year. Which implies they are well adapted to temperature extremes. Which implies they are innately low in terms of temperature sensitivity
* Unlike trees in humid climates, these trees cannot count on warm season precipitation for moisture. The lion’s share of moisture is from snow melt. This would imply that the main control on growth is the presence or absence of a robust snow pack at just the right time. In the climate they live in, precipitation also varies in the extreme from year to year. Snowpack varies in the extreme. I would be inclined to view Bristlecones as being more of a moisture proxy than a temperature one. And moisture in that area does not necessarily correlate in any way to GMT.

Rob, thanks for coming here. You’re a great man. I’m just an internet maggot. blabla. I’m going to be wearying with the amount of questions and even at times trying to verify things that “should be self-evident”. However, the devil is often in the details and if we don’t check some definitions at times, we get no where in discussion. And you’re under no obligation to answer of course. However, where else will you get people willing to parse a subject as dry as this?

1. The “NA Mean from 2006 paper” seems like a bit of an indefinite, non-physical series. I’m not saying it’s bad, but how do we know it’s good?

2. Why did you “want to use as high lattitude as possible”? What if lower lattitudes don’t respond the same as high lattitudes? How can you get a NA mean? What do we see with instrumental data for the last 100 years? Do southerly and northerly lattitudes track?

3. I agree with your comment about the MWP. It almost seems like the dramatic thing about the series is what it does in terms of “getting rid of the MWP” (and who the heck said that…let’s get Drudge on it). And if the BCPs are real than it is an interesting effect that MWP went down in this area. I could see that even being possible. Then Steve would need to sharpen his argument on geo weighting and the like.

4. I agree about the “surprisingly strong signal”. I can see it. Could you characterize it mathematically? R2 value? p or t test? Any idea why it occurs? (I guess one reason might be both series tracking climate, but any other ideas?)

6. “Now, I do not deny that all sorts of other factors may also influence growth and I am sure someone will say – “well how about all the none explained variance?.’ Well – Dendro-reconstructions generally explain anywhere from 30-60% of the variance of the climate parameter that one is trying to reconstruct. This is a modeled mean response over a particular [calibration] time-period and does not look at the more complex situation for each year. Using regression, it is easy to test if multiple climate parameters (i.e. temperature and precipitation) effect the growth of trees. It is generally the case, that if the tree site is carefully selected (i.e. high elevation/latitude treeline for a temperature signal), then precipitation will have a minimal effect on growth and over the period of calibration, the correlation with precipitation will be close to zero.”

Well…are you saying that we follow the careful procedure ALREADY and still get 40-70% unexplained variance? Or that when we follow these careful procedures, the 40-70 goes away? (Seguing: your comment about careful site selection is fascinating. We should have very good references for this procedure and very good records kept of the particulars if this affects the answers so much! Also, do people do calibration to prove (for their particular study) that there is minimal precip effect? In the same place where they compare to instrumental temp, do they also compare to instrumental precip?)

7. Why the heck would CO2 affect one type of tree more than the other? Anyhoo, I think what you are saying is as simple as that the blade is sharper in NA mean than in BCP. That doesn’t tell us anything about CO2 or not. (And I’ve always felt that Steve’s reliance on a posited, alluded, speculated, not-proved CO2 effect from the Ibso weirdos was a weak straw…)

8. “…there is no evidence of this at all…” What is “evidence of this”. Give me some numbers, etc. I think Steve has a weak reed with the Idsos and you have a weak reed with the reverse.

9. “comparison of the NA RCS mean with the recently published annual temperature reconstruction for the Colorado region from Salzer and Kipfmueller (2005). ” How is the RCS NA Mean different from the NA mean? Love to see them plotted together. Why do you compare a to b then C to d? Why not a to b and a to d?

10. “They also used BP data – independent to the data I used.” Independant to the earlier BCP series (figure 1)?

11. “There model explains 46% of the variance (DW = 1.64), so is appears to be a much stronger temperature proxy than the one I showed in Figure 1. ” Eh…no. No. NO. You’re doing that Rob Wilson circular logic thing again. FYI: It shows that it has better correspondance to the NA mean reconstruction (and not even the same one)…not that it was a better temp proxy. Don’t be as stupid as the HOU office with Enron.

12. “Again we see reasonable coherence between 1100-1900 and again the BP data do not show higher values in the 20th century than the NA RCS mean. ” Agreed. Of course, you should make it mathematical. Numbers, buddy, numbers. “Reasonable” is fluffy Mannian crap that gets Burger and Cubashed up the ass.

13. “Salzer and Kipfmueller did not use RCS for detrending so it is possible that they have lost some long term information.” Huh? Why does failure to age-correct lose long term information? (I guess it could screw things up, but would it “lose information” or would it “give wrong information”.)

TCO,
If you added a bit more profanity and a few more insults you would be assured that Rob would not answer your questions (which seems to be your intent). I don’t know why you want that outcome, maybe so that you can crow that he could not answer your q’s. Anyway, have a few more beers and try again.

If there were some sort of prize for “best post” at Climate Audit, I would nominate Rob. This is perhaps the most substantive challenge to Steve posted at the blog. It’s also highly relevant as the bcps are central to many of M&M’s criticisms of the multiproxy studies.

I look forward to Steve’s response, as well as the answers to TCO’s questions. I’d like to throw another into the ring, however. I’ll call it (14) to add to TCO.

14. My understanding is that the objection to the bcps is based on the fact that their 20th century growth spurt is anomalous, meaning that the ring widths do not correlate with nearby instrumental temperature records (for whatever reason). I believe even Bradley (or is it Hughes?) concedes that it’s a mystery.

My take-out is that, if the bcps do not track the instrumental record in the 20th century, how can they be considered reliable temperature proxies for the past? Whether the bcps track Rob’s high-latitude NA mean in the 20th century seems beside the point. The bcps can only respond to their immediate climate – they can’t respond to to some sort of notional NA temperature.

Don’t feel bad, Steve B. I think you’re quite a tall-tree Troll. But I actually made much the same point as jae long ago when TCO was here the first time. Still, Steve M does seem to like his time wasters because, I think, they are actually concerned with the subject at hand. You, Peter, Tim, Dano, etc. almost never discuss the science and instead engage in ad hom attacks of one sort or another. As such you’re fair game for Steve’s hangers-on.

Re #20: Dave, it’s difficult to focus on science here when the hangers-on (good phrase) behave as they do. Why do you suppose Steve encourages them? On occasion when I do post something substantive I don’t exactly feel rewarded. But as you well know I spend a whole lot of time on the science elsewhere.

I actually think you are among the best of the AGW guys posting at Climate Audit.

Could you provide an example from among your “substantive” posts that have been inadequately dealt with by Steve and/or others? As a condition, can we limit to stuff dealing with the paleo reconstructions, which is what the blog is about?

Good Morning,
Firstly, I leave for a two week holiday tomorrow, so my silence should not be taken as impoliteness or a general weariness of CA.

There are a lot of issues/questions and I will endeavor to answer as many of them as I can. These are in no particular order.

1. I stated “Take home message – I do not think the BP data are as bad as Steve would have us believe”. However, I never said that they were GOOD either. With a correlation of only 0.38 (explained variance =14%), these data can hardly be defined as a strong temperature proxy. There are certainly issues with BP data, but I only added the post to add a little balance to the argument against them. The work by Salzer and Kipfmueller shows that BP data can express a valid temperature signal.

2. Some people asked for statistical measures of coherence between the NA mean and BP series. Firstly, the NA mean in both Figures 1 and 2 was the same RCS composite chronology (should have labeled it better – sorry). For the 1100-1985 period, the correlation of the NA mean series with my BP series and the Salzer series are 0.40 and 0.24 respectively. When the data are smoothed with a 20 yr. gaussian filter, the correlations are higher at 0.59 and 0.37 (and no, I will not generate the significance of these correlations after adjusting the degrees of freedom for 1st AC). For the full period of overlap (713-1985), the correlations are lower (as we would expect from qualitatively looking at the figures) – unfiltered = 0.21 and 0.20 – smoothed = 0.32 and 0.33. For completeness sake, my BP data correlates with the Salzer recon at 0.27 (1100-1985) and 0.17 (713-1985). For the smoothed versions, the correlations are 0.43 and 0.33.

3. w.r.t. “How can you resolve between CO2 causation and temperature causation?” Well – dendrochronologists have been looking for CO2 signals in tree-rings for the past few decades. I have never read any definitive conclusions. The Bristlecone pine data are often cited as being one of the few examples where there COULD be a CO2 inflation effect. However, in the situation where both temperature and CO2 (and possibly precipitation) increases, then can we really identify which one is controlling long term changes in tree-growth? This is an important question, I am sure my answer will not please all at CA.
Firstly with regards to the Salzer and Kipfmueller study, they state “Unequaled post-1976 growth in these higher elevation chronologies has been attributed to CO2 enrichment (Graybill and Idso, 1983; LaMarche et al., 1984), but may be the result of unusually warm/wet conditions. In particular, a combination of wet springs and longer growing seasons would favor enhanced growth at higher elevations.” They go on to show in their paper that indeed, for the recent period, BP levels are high due to unusually warm/wet conditions. I will admit to having only skimmed the paper. Perhaps some holiday reading.
At larger scales, if CO2 fertilization was a significant factor, then we would see almost ALL trees showing accelerated growth levels in the 20th century. We do not and in fact, in some cases, we see a decline in productivity at sites since the mid 20th century. Part of this is related to this divergence problem (more later on that), but this is phenomenon for only certain regions.
Finally, a very interesting project being undertaken by Swiss researcher can be read here:http://pages.unibas.ch/botschoen/scc/index.shtml
They have literally taken a small section of forest and riddled the whole area with tubes giving off CO2 directly into the local environment. I will not go into details. However, their results are quite contradictory. Some trees species have shown some growth increase – others have not. Essentially, overall, they have shown that without any other limiting factor, CO2 is of course important. However, when temperature, precipitation and nutrients are also included, it looks like CO2 (which is not a limiting factor) often has little influence on tree productivity.

4. w.r.t. looking for “Hockey Stick” signals. Globally temperatures have been increasing since at least the early 19th century – yes, I know there are regional differences, but I am talking about a global or at least a NH average. Therefore, if one is working with proxy data that are sensitive to temperature changes, then we would expect to see increasing trends over the last two centuries. This trend is NOT an artifact of the statistical methods.

5. w.r.t. the Durbin-Watson statistical test for 1st order autocorrelation in a regression model’s residuals. I have been receiving conflicting comments about the use of this statistic. Steve has been stating for quite some time that this should be quoted when correlations between two series are made. However, many people (including Steve recently?) have said that this statistic is not relevant when the independent and dependent variables show strong persistence (i.e. lots of trend). I am keen to learn here – is its use correct or not?
Personally, I think dendroclimatology is one of the most empirically robust disciplines in the palaeoclimate community. This probably simply reflects the fact that we have soooo much data. When I develop a reconstruction, I follow some fairly simply steps to validate the model – independent verification using the correlation coefficient, reduction of error (RE), coefficient of efficiency (CE) and sign test (ST) (plus others). I will not detail what these statistics are. It is all in the literature. Further assessment is testing the model residuals for autocorrelation (i.e. DW) and for linear trends. If a reconstruction passes all these tests, then you can assume that it is probably fairly robust – at least within the bounds of the assumptions made in the modeling – i.e. the relationship modeled in the calibration period holds true for the whole reconstruction. Some at CA have challenged this assumption – this is simply the Principle of Uniformitarianism (James Hutton, 1726-1797). For all palaeoclimate studies, we have to follow this principle. It would be nice to take a time-machine and go back to the Maunder Minimum and MWP and take 50 years of temperature data. Such data would help validate and constrain the estimates in our reconstructions. Alas, this probably will not be possible in my life time and it would nullify the use of palaeo proxies anyway. Seriously, we have no option to make such an assumption and hopefully through multi-proxy comparison, we can make some sort of mutual validation.

6. The Divergence Issue. This is possibly one of the more important issues that we (dendroclimatologists) need to address at this time. However, from the outset, divergence is not seen at all sites through the world. In our 2006 paper, we CHERRY PICKED our sites – Oh god yes, I state it publicly. HOWEVER, we did not cherry pick for a HS signal which is what Steve would have you believe. Rather we picked the longest chronologies (that were locally “temperature sensitive”) as possible. The reality is that some of these sites unfortunately do show a divergence against local temperature data – 6 in fact of the 19 we used (plus 3 other possibilities), of which many of them are located in NW America (Alaska and the Yukon). As well as the NA mean series, we also developed a Eurasian mean series. When this is compared to mean annual temperatures for Eurasia (north of 20 degrees), no divergence is noted between the TR mean series and the instrumental data. In our NH reconstruction at least, the divergence is real (post 1985), but it exists because of the use of TR sites from the American north-west. We are currently exploring this issue further in a set of papers that hopefully will come out over the next 12 months or so. Be patient. In our 2006 paper, we clearly make the cautionary statement that as we cannot model late 20th century temperatures, then this questions our ability to model similar earlier warmer periods (e.g. the MWP). We also state that there is probably not enough data prior to ~1400 to make any definitive claims about MWP conditions at NH scales.

You’ve been: an author in a published article; have turned up to a site which is intended to disassemble climate-related articles; have produced even-handed, considered responses to criticisms; have admitted that uncertainties and weaknesses exist in certain aspects of your reconstructions…in short, you’ve been completely professional about the entire exercise.

You’ve already excelled yourself in the eyes of this forum participant by doing so.

I would trust that the inevitable criticisms are equally thoughtful – “constructive” is a word which is often used.

I do, personally, have many problems with any tree species being used as a temperature proxy, but appreciate your position that they may have merit, although you have the honesty to admit their potrntial issues.

As a lawyer, I’ve had colleagues criticize my trust deeds/contracts/etc. It hurts, but it invariably makes my work better and clarifies whether or not points of difference are me being wrong/them being wrong/points being debateable.

So, these kind of processes are not just limited to science, much less the climate sciences. They’re healthly and productive aspects of every calling or discipline.

In fact, being older and regularly exposed to criticism, I’m never offended by it anymore, even though some of those older agreements have come back to haunt me.

Nobody’s perfect and my agreements are one hell of a lot better these days.

Rob,
Thank you for taking the time to compose an intelligent response to our questions. You obviously took some time composing it. I guess my take-away at this time is that due to the uncertainties we really can’t come to any conclusion in regard to the current temperature trends, re: natural or not. Given that, I find the IPCC summaries very misleading, and most of the press releases outright lies. I’m really interested in your assessment in this regard: Do the current paleoclimate reconstructions support a conclusion that the current temperature trends are or are not likely outside the normal natural variations? If such a conclusion is justified, why do you think so? I’m not trying to trap you here in regard to your support (or opposition) to the AGW theory, which is why I worded it they way I did, but it seems to me that this question is at the center of what Steve is doing on this blog.

“but it seems to me that this question is at the center of what Steve is doing on this blog.”

It may be the naked royalty sitting in the corner, but from what Steve has said before I don’t beleive it is at the center of what he is doing.

WHat Steve is doing is Science pure and simple. He saw some results (graphs) that he found a little hokey (as well as Hockey) and he decided to look into them for his own personal reasons. So far as I understand it, the center of Steve’s work is to delve into the statistical accuracy of the Mann et all Hokey.. I mean Hockey stick.

There are things I know for sure. I know my name, address, phone and social security numbers. There are things I don’t know but can find out easily with near 100% certainty for example the exact unpaid balance of my mortgage. There are things I don’t know but can find out with some work and with less than 100% certainty such as exactly how tall I was on my ninth birthday — the school recorded my height periodically and if I can recover those records and apply a little interpolation I can come up with a number that is close to the truth.

There are things I’ll never know such as how far the earth is from Alpha Centauri to the nearest meter. It is unknowable.

The statement, “It is now warmer than it has ever been in the last 1,000 to 2,000 years,” is one that cannot be proved or disproved with 100% certainty. It is unknowable. No person or persons wrote down the temperature every day over the last two millennia and no matter how hard we search we’ll never find such a record. So we look for ways to infer the temperature and we find tree rings, ice cores, glaciers, sediments and other indirect records of historical temperatures. Each has its strengths and faults. None is perfect. None is without both champions and detractors.

Steve M. has said often that he doesn’t have all the answers and now Rob Wilson has added his voice to the “I know a lot but I don’t know everything” truth seekers. He knows more than a lot.

It is important that we assign a probability to the truth of the “It is now warmer … ” statement and that is what this blog and much of climate research is about. Steve M’s work here applies what he knows about statistics to what he has learned about paleoclimatology and he has done a fine job forming an opinion and communicating it to his readers. And because he has done it using a blog with comments, it is far broader in scope, faster moving and frequently more entertaining than a peer reviewed journal. What we should learn from Climate Audit is that anyone who says the “It is now warmer … ” statement is true for sure is just as mistaken as someone who says it is false for sure. And I am 100% sure about that.

And finally, if Steve M’s views prevail, it will be interesting to go back and see what part Climate Audit played in his ascendancy.

I think the real value is Steve showing that the statistical methods and the underlying data cannot bear the weight of some climate scientists’ feelings of certainty. The real take-home message is don’t try to reconstruct past climate without proper calibration of your thermometer together with an understanding of its limitations.

The Bristlecone pine data are often cited as being one of the few examples where there COULD be a CO2 inflation effect. However, in the situation where both temperature and CO2 (and possibly precipitation) increases, then can we really identify which one is controlling long term changes in tree-growth?

Firstly with regards to the Salzer and Kipfmueller study, they state “Unequaled post-1976 growth in these higher elevation chronologies has been attributed to CO2 enrichment (Graybill and Idso, 1983; LaMarche et al., 1984), but may be the result of unusually warm/wet conditions. In particular, a combination of wet springs and longer growing seasons would favor enhanced growth at higher elevations.” They go on to show in their paper that indeed, for the recent period, BP levels are high due to unusually warm/wet conditions.

There is a problem with Rob Wilson’s analysis. A problem of dissociation of effects using an additive model (TCO’s favorite topic) when there are multiple fertilization effects (temp,precip,CO2,etc) which are likely all positive synergistic.

Tell me. If growth, temp, precip, CO2 interact in the following way, with all effects & synergistic interactions positive:

G = T + P + C + T*P + T*C + P*C + T*P*C

then what would happen if your reconstruction model was mis-specified as:

G = T + P

Ans: If the 20th c. were a bit warmer, a bit wetter, a bit richer in CO2, a bit more whatever, then your reconstructions would be a lot more than a bit hockey-stick shaped. Why? You’ve incorrectly attributed the multiplicative synergy to independent additive effects through a model mis-specification.

Re: bender (#33),
Bump.
People are speculating now about CO2 fertilization of Yamal larch. I want to highlight that this has been discussed already, years ago, in the context of a fuller model of bristlecone pine growth – a model that includes not just precipitation, but also interactions between terms – especially temperature * precipitation.

As the living cambium increases in perimeter & surface area over time these nutritional demands become unsustainable and portions of the cambium die back, followed by larger parts of the stem and roots.

The impact of 19th century sheep grazing on trees in the U.S. Southwest is quite profound. There’s an interesting discussion here:

As a result of these excessive stocking numbers, once rich grasslands were seriously degraded even before the turn of the century, after less than a human generation of use. By the early 1900s, overstocking of sheep on many middle-elevation mesas and in the area’s highlands had brought forest regeneration to a halt. The forest floor in some places was "as bare and compact as a roadbed." The fire ecology of the region’s forests, particularly the once grass-rich ponderosa pine forests, was drastically altered, causing significant long-term changes to their structure and composition.

Here’s an interesting picture of sheep grazing in an area that is not presently grassland:

Also look at the picture at this site of areas that have been grazed and not grazed. I don’t know whether sheep could have affected bristlecones in the 19th century and denuded alpine meadows, which have not regenerated, but it should be examined by anyone proposing to rely on bristlecones as a unique arbiter of world climate. See this discussion in MM05b:

There is a published reference to the introduction of large commercial sheep flocks in the late 19th century in the White Mountains CA [St. Andre et al. 1967], where the key sites of Sheep Mountain and Campito Mountain are located. The founder of the Sierra Club, John Muir, complained of the depredations of sheep in the Sierra Nevadas (adjacent to the White Mountains) as “hoofed locusts” [Muir, 1911]. Carl Purpus, a late 19th century botanical collector in the Sierra Nevadas, stated in 1896 that commercial flocks had cleaned out all grass to the top of Old Mt Whitney [present-day Mount Langley, which reaches 4,280 m] [Ertter, 1988]. Allen (pers. comm., 2004) said that there was a large commercial sheep trail at Jicarita Peak NM, another bristlecone pine site studied by LaMarche and Stockton [1974].

As someone that spent many years growing plants and trees, it seems to me that all these sheep eating the undergrowth and leaving millions of little nitrogen laden balls of fertilizer in their wake are quite likely to have a pronounced positive impact on the growth rate of any trees they have been grazing under.

Anyone that has done organic farming could tell you that poop of any kind is one of the best accelerators of tree growth there is.

If this was happening a bit before and/or during the calibration period the temperature/soil-moisture signal will be well and truly magnified by the fertilizer signal, making growth ring comparisons to earlier times when sheep were not crapping under the trees a total nonsense.

Calling Dr. Wilson: #33.
If bcp growth is a synergistic function of P, T (i.e. drought-limited) then how do you reconstruct T, using S&K’s (2005) method of assuming high-elev bcps are T-limited and low-elev bcps are P-limited?

Steve: Shouldn’t it be proven that high-elev BCPs are T-limited? The California BCPs only go to an altitude that is measurably lower than the Colorado treeline at equivalent latitude. I suspect that precip still affects upper treeline.

If growth, temp, precip, CO2 interact in the following way, with all effects & synergistic interactions positive:

G = T + P + C + T*P + T*C + P*C + T*P*C

then what would happen if your reconstruction model was mis-specified as:

G = T + P

Ans: If the 20th c. were a bit warmer, a bit wetter, a bit richer in CO2, a bit more whatever, then your reconstructions would be a lot more than a bit hockey-stick shaped. Why? You’ve incorrectly attributed the multiplicative synergy to independent additive effects through a model mis-specification.